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Gender Recognition System Based on Human Face Picture

Mazurkiewicz J.

Journal of Polish Safety and Reliability Association

2017

Vol. 8, No. 1

97--104

The paper presents the analysis and discussion of gender recognition based on human face picture. The research combines different features selection techniques with the set of softcomputing classifiers. We are looking for not very complicated, fast and sensitive approach to create the theoretical basis for real safety systems where the correct “on-line” gender recognition is necessary. We start from the already known differences between the female and male face. This is the key point to tune the preprocessing mechanisms. We propose the quite classic classifiers, but we focus on sensible correlation between the feature extraction and the actual classification. The significant set of the results are discussed and the best solutions are pointed. All tests were realised based on the well known base of face pictures with added set of our own collection. The proposed solution can be an essential tool for the monitoring systems, safety guards and systems to point the dangerous situations based on video data.

Efficient face recognition based on weighted matrix distance metrics and 2DPCA algorithm

Rouabhia C., Tebbikh H.

Archives of Control Sciences

2011

Vol. 21, no. 2

207-221

In this paper, a new similarity measure is developed for human face recognition, namely, weighted matrix distance. The key difference between this metric and the standard distances is the use of matrices and weights rather than the vectors only. The two feature matrices are obtained by two-dimensional principal component analysis (2DPCA). The weights are the inverse of the eigenvalues sorted in decreasing order of the covariance matrix of all training face matrices. Experiments are performed under illumination and facial expression variations using four face image databases: ORL, Yale, PF01 and a subset of FERET. The results demonstrate the effectiveness of the proposed weighted matrix distances in 2DPCA face recognition over the standard matrix distance metrics: Yang, Frobenius and assembled matrix distance (AMD).